code: def _finalize(
      history: List[Union[Keep, Insert, Remove]],
      state_dict_a: Dict[str, torch.Tensor],
      state_dict_b: Dict[str, torch.Tensor],
      scale: float,
      threshold: float,
  ) -> List[Union[Keep, Insert, Remove, Modify]]:
      out = cast(List[Union[Keep, Insert, Remove, Modify]], history)
      for i, step in enumerate(out):
          if isinstance(step, Keep):
              a_tensor = state_dict_a[step.key]
              b_tensor = state_dict_b[step.key]
              with torch.no_grad():
                  dist = (
                      scale
                      * torch.nn.functional.mse_loss(
                          a_tensor, b_tensor
                      ).sqrt()
                  ).item()
              if dist > threshold:
                  out[i] = Modify(step.key, step.shape, dist)
      return out
  
imports: import argparse
  import logging
  from typing import Union, Dict, List, Tuple, NamedTuple, cast
  import termcolor
  import torch
  from allennlp.commands.subcommand import Subcommand
  from allennlp.common.file_utils import cached_path
  from allennlp.nn.util import read_state_dict
label_desc: Use image node_id 1 for calling a global function with example usage: _finalize(history, state_dict_a, state_dict_b, scale, threshold) and returns: out